AIMC Topic: Risk Assessment

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Machine learning-based risk modeling for safety-focused learning curve assessment in robotic left-sided colorectal cancer surgery.

Journal of robotic surgery
The transition from laparoscopic to robotic surgery for left-sided colorectal cancer raises safety concerns during the learning curve, particularly when complex cases are preferentially selected for the robotic platform. We evaluated a machine learni...

Development and external validation of machine learning approaches for risk prediction of cardiovascular disease in individuals with schizophrenia: a nationwide Swedish and Danish study.

BMJ mental health
BACKGROUND: Currently available cardiovascular disease (CVD) risk prediction tools may underestimate the risk in individuals with schizophrenia. OBJECTIVE: To develop and externally validate 5-year CVD risk prediction models for people with schizophr...

Development of Venous Thromboembolism Risk Prediction Models Based on Whole Blood Gene Expression Profiling Using 20 Machine Learning Algorithms: Comprehensive Analysis Study.

JMIR medical informatics
BACKGROUND: There is a lack of venous thromboembolism (VTE) risk prediction models based on gene expression information. OBJECTIVE: This study aimed to construct a VTE prediction model based on whole blood gene expression profiling, by performing a c...

External validation of the IHXGboost-P model to predict incisional hernia after midline laparotomy.

Hernia : the journal of hernias and abdominal wall surgery
BACKGROUND: Incisional hernia (IH) is a significant complication that occurs after midline laparotomy and is associated with high morbidity and economic impacts. A fundamental goal of preventing IH is to determine which patients are considered low- o...

Non-invasive blood pressure monitoring using wearables for cardiovascular risk assessment: a systematic review.

Archives of gynecology and obstetrics
PURPOSE: Cardiovascular diseases are the leading causes of mortality in women worldwide, with hypertension being a major risk factor. While traditional blood pressure monitoring techniques rely on cuff-based measurements, wearable devices offer a pro...

Development and external validation of an interpretable machine learning-based model for obesity risk prediction in 2-18-year-old children and adolescents in Beijing and Tangshan.

Journal of global health
BACKGROUND: The multifactorial mechanisms driving childhood obesity, a global public health challenge, are yet to be fully elucidated. We aimed to develop and externally validate three widely applied machine learning models alongside logistic regress...

Dynamic Ensemble Selection for Early Detection of Deep Vein Thrombosis in Fracture Patients.

Journal of medical systems
Deep vein thrombosis (DVT) in fracture patients is often clinically silent, with a high incidence of thrombosis and associated mortality. Static machine learning methods struggle to address the challenge of early DVT diagnosis due to their inability ...

Explainable AI for Predicting Mortality Risk in Metastatic Cancer: Retrospective Cohort Study Using the Memorial Sloan Kettering-Metastatic Dataset.

JMIR cancer
BACKGROUND: Metastatic cancer remains one of the leading causes of cancer-related mortality worldwide. Yet, the prediction of survivability in this population remains limited by heterogeneous clinical presentations and high-dimensional molecular feat...

Environment and CVD: moving from Risk Prediction to Risk Management.

Current atherosclerosis reports
PURPOSE OF REVIEW: We attempt to provide a framework for cardiovascular risk assessment related to environmental pollutants to enhance awareness of risk posed by environmental risk factors and highlight approaches for risk intervention.

Development of an explainable prediction model for the risk of moderate-to-severe obstructive sleep apnea in children.

European journal of pediatrics
UNLABELLED: Early identification of children at high risk for moderate-to-severe obstructive sleep apnea (OSA) is crucial for timely intervention, yet is often hindered by limited access to polysomnography (PSG). We aimed to develop an interpretable ...